检索结果-内蒙古大学图书馆

您好，读者！请登录

内蒙古大学图书馆

首页
概况
党建
资源
服务
科研支持
- 论文收录引用证明
- 科技查新
知识产权
档案馆
帮助

咨询与建议

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

您的常用邮箱：*

您的手机号码：*

问题描述：

当前已输入0个字，您还可以输入200个字

全部搜索
期刊论文
图书
学位论文
标准
纸本馆藏
外文资源发现
数据库导航
超星发现

高级检索

时间限定

出版年份：

文献类型

图书期刊文献学位论文多媒体

馆藏选择

电子馆藏纸本馆藏

核心期刊

全部期刊 SCI 收录期刊 SSCI 收录期刊 EI 收录期刊 CSCD 收录期刊 CSSCI 收录期刊

语言

中文英文

文献类型

期刊文献图书学位论文标准纸本馆藏

帮助

文字说明：

T=题名（书名、题名），A=作者（责任者），K=主题词，P=出版物名称，PU=出版社名称，O=机构（作者单位、学位授予单位、专利申请人），L=中图分类号，C=学科分类号，U=全部字段，Y=年（出版发行年、学位年度、标准发布年）

检索规则说明：

AND代表“并且”；OR代表“或者”；NOT代表“不包含”；(注意必须大写,运算符两边需空一格)

检索范例：

范例一：(K=图书馆学 OR K=情报学) AND A=范并思 AND Y=1982-2016
范例二：P=计算机应用与软件 AND (U=C++ OR U=Basic) NOT K=Visual AND Y=2011-2016

分类表

所选分类

>> <<

限定检索结果

文献类型

6,882 篇 会议
159 篇 期刊文献
3 册 图书
2 篇 学位论文

馆藏范围

7,046 篇 电子文献
0 种 纸本馆藏

日期分布

学科分类号

3,979 篇 工学
- 2,184 篇 计算机科学与技术...
- 1,231 篇 电气工程
- 1,219 篇 软件工程
- 844 篇 信息与通信工程
- 476 篇 光学工程
- 430 篇 控制科学与工程
- 426 篇 生物医学工程（可授...
- 327 篇 仪器科学与技术
- 322 篇 电子科学与技术（可...
- 280 篇 机械工程
- 249 篇 生物工程
- 148 篇 材料科学与工程（可...
- 117 篇 测绘科学与技术
- 98 篇 航空宇航科学与技...
- 92 篇 核科学与技术
- 87 篇 化学工程与技术
- 70 篇 动力工程及工程热...
- 70 篇 交通运输工程
2,307 篇 理学
- 1,730 篇 物理学
- 448 篇 数学
- 266 篇 生物学
- 120 篇 统计学（可授理学、...
- 117 篇 系统科学
- 105 篇 化学
1,191 篇 医学
- 924 篇 临床医学
- 334 篇 特种医学
- 90 篇 基础医学(可授医学...
503 篇 管理学
- 429 篇 管理科学与工程(可...
- 100 篇 图书情报与档案管...
- 81 篇 工商管理
55 篇 农学
51 篇 法学
32 篇 经济学
15 篇 教育学
12 篇 文学
8 篇 军事学
8 篇 艺术学

主题

1,015 篇 image quality
331 篇 image processing
327 篇 image coding
323 篇 image reconstruc...
296 篇 image segmentati...
274 篇 feature extracti...
249 篇 image enhancemen...
229 篇 cameras
199 篇 visualization
190 篇 deep learning
135 篇 image edge detec...
128 篇 image resolution
123 篇 humans
120 篇 psnr
117 篇 accuracy
115 篇 image compressio...
107 篇 real-time system...
105 篇 training
100 篇 three-dimensiona...
100 篇 image color anal...

机构

10 篇 chitkara univers...
9 篇 eastman kodak co...
8 篇 purdue univ sch ...
8 篇 johns hopkins un...
8 篇 beijing universi...
7 篇 univ chinese aca...
7 篇 univ arizona col...
7 篇 chinese acad sci...
7 篇 johns hopkins un...
7 篇 university of ch...
6 篇 shandong institu...
6 篇 univ sci & techn...
6 篇 tianjin univ sch...
6 篇 school of electr...
6 篇 school of intell...
6 篇 univ arizona dep...
6 篇 univ penn dept r...
5 篇 nanyang technol ...
5 篇 institute for in...
5 篇 peking univ peop...

作者

17 篇 triantaphillidou...
10 篇 williams don
9 篇 allebach jan p.
9 篇 pedersen marius
9 篇 agaian sos
8 篇 jacobson ralph e...
8 篇 bovik alan c.
8 篇 zhai guangtao
8 篇 burns peter d.
7 篇 busch christoph
7 篇 liu ming
7 篇 panetta karen
7 篇 chetouani aladin...
7 篇 kupinski matthew...
7 篇 pfefer joshua
7 篇 wueller dietmar
7 篇 heynderickx ingr...
7 篇 siewerdsen j. h.
6 篇 zhang lei
6 篇 liu xinwei

语言

6,914 篇 英文
83 篇 其他
50 篇 中文
6 篇 土耳其文
1 篇 西班牙文
1 篇 法文

检索条件"任意字段=Conference on Image Quality and System Performance XII"

共 7046 条记录，以下是21-30 订阅

全选清除本页清除全部题录导出标记到"检索档案"

详细简洁

排序：

LTS: A DASH Streaming system for Dynamic Multi-Layer 3D Gaussian Splatting Scenes 25

LTS: A DASH Streaming System for Dynamic Multi-Layer 3D Gaus...

引用

16th ACM Multimedia systems conference, MMSys 2025

作者： Sun, Yuan-Chun Shi, Yuang Lee, Cheng-Tse Zhu, Mufeng Ooi, Wei Tsang Liu, Yao Huang, Chun-Ying Hsu, Cheng-Hsin National Tsing Hua University Hsin-Chu Taiwan National University of Singapore Singapore Singapore Rutgers University PiscatawayNJ United States National Yang Ming Chiao Tung University Hsin-Chu Taiwan

ISBN: (纸本)9798400714672

We present a novel DASH-based streaming system for dynamic 3D Gaussian Splatting (3DGS) scenes, addressing the challenges of streaming large amounts of 3DGS data over diverse and dynamic networks. Our Layer, Tile, and Segment Adaptive streaming (LTS) system combines three key features: (i) multi-layer streaming, which adapts to diverse client capabilities while balancing visual quality and bandwidth usage, (ii) tiled streaming, which reduces unnecessary data transmission by focusing on the user's viewport, and (iii) segment streaming, which divides dynamic 3DGS scenes into segments, letting clients request them dynamically to handle network fluctuations. Our experimental results demonstrate that our LTS system achieves superior performance in both live and on-demand streaming of dynamic 3DGS scenes compared to the baselines. For example, in live streaming, LTS could achieve up to 99.70% reduction in missing frames on average and deliver a maximum PSNR (Peak Signal-to-Noise Ratio) improvement of 10.08 dB. In on-demand streaming, LTS could reduce the freeze time by up to 92.01%, and increase the synthesized view quality by up to 5.14 dB in PSNR and 0.11 in SSIM (Structural Similarity Index). Our source codes are available at: https://***/AIINS-NTHU/LTS-DASH-Streaming-system-for-3DGS. © 2025 Copyright is held by the owner/author(s). Publication rights licensed to ACM.

关键词： image coding

来源：评论

学校读者我要写书评

暂无评论

Implementation of a Computer Vision system for Cucumber Classification Based on quality Standards in the Agro-Food Industry 15

Implementation of a Computer Vision System for Cucumber Clas...

引用

Decima Quinta Conferencia Iberoamericana de Complejidad, Informatica y Cibernetica, CICIC 2025 en el contexto de the 16th International Multi-conference on Complexity, Informatics, and Cybernetics, IMCIC 2025 - 15th Ibero-American conference on Complexity, Informatics and Cybernetics, CICIC 2025 in the context of the 16th International Multi-conference on Complexity, Informatics, and Cybernetics, IMCIC 2025

作者： León León, Ryan A. Concepcion Vazquez, Axel E. Valqui Moya, Daniel J. Bailon Diaz, María M. Huaman Mosqueira, Fernando J. Salcedo Marreros, Jaime C. Universidad Privada del Norte Trujillo 13001 Peru

ISBN: (纸本)9781950492824

The objective of this work was to implement a computer vision system based on convolutional neural networks (CNN) and the YOLOv8 model to classify cucumbers according to quality standards in the agrofood industry. A total of 869 cucumber images were collected in real production conditions, which were labeled and processed using the COCOdataset platform. Using Google Colab and PyTorch, the YOLOv8 model was trained, with performance optimized through image preprocessing techniques and data augmentation. The results showed a 95% accuracy in cucumber classification. This high accuracy supports YOLOv8's capability to detect and classify objects in real-time, proving to be an effective and precise solution for automating the selection process in the agro-food industry. Copyright 2025. © por el International Institute of Informatics and systemics.

关键词： Convolutional neural networks

来源：评论

学校读者我要写书评

暂无评论

An Efficient image Denoising Using Convolutional Neural Network 6th

An Efficient Image Denoising Using Convolutional Neural Netw...

引用

6th International conference on communication and computational technologies, ICCCT 2024

作者： Bodhale, Vaishali Vijayalakshmi, M. Chopra, Shalu Vivekanand Education Society’s Institute of Technology Sindhi Society Chembur Mumbai India

ISBN: (纸本)9789819774258

Pre-processing plays an essential role in any image processing application. The quality of features is a function of how well the input images are presented to the feature-extracting module or a classifying network. A clean image produces a non-redundant, distinct feature set that assists a classifier system. A variety of noises influence an image due to acquisition systems, interference, and storage. image denoising, thus being an essential and crucial concern, removes unwanted artefacts from significant details of the image. Autoencoder and Decoder constructed using deep-learned convolutional layers can de-noise images with higher performance metrics. The work in this paper proposes a 7-layer convolutional network (Autoencoder-Decoder) for eliminating noise contents from test images. The images are from the CIFAR-10 dataset available with the Kaggle data store. The original image is scrambled, encoded using a random key, and subjected to Gaussian noise at the transmitter. It is further decoded with the same key to obtain a noisy image. The noisy images from the input to the convolutional neural network (CNN) and the original images as the targets. The network is trained using 4000 CIFAR-10 images and tested using 1000 images. Experimental analysis showed remarkable results in terms of peak signal-to-noise ratio (PSNR) and structural similarity (SSIM). The average metrics over 10 images showed that the PSNR is about 34.861, and the recovered image retains the original image up to 0.996% (SSIM). © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Autoencoder CIFAR-10 Convolutional layers Convolutional network Deep-learned Gaussian noise image denoising PSNR SSIM

来源：评论

学校读者我要写书评

暂无评论

Application of Intelligent Algorithm in image Recognition Under the Vision of Artificial Intelligence 14th

Application of Intelligent Algorithm in Image Recognition Un...

引用

14th International conference on Frontier Computing, FC 2024

作者： Xu, Lei School of Teacher Education Anhui Open University Anhui Hefei230001 China

ISBN: (纸本)9789819627936

Artificial Intelligence (AI) is an emerging technical discipline that simulates, expands and expands human intelligence. This is the study of how to make computers mimic certain intellectual behaviors of people. The essence of artificial intelligence is to let the computer have the perception, thinking, learning, reasoning and other functions. With the rapid development of network technology, the application of artificial intelligence is more and more extensive, and image recognition is one of the key links. In this paper, the principle of intelligent algorithm is described, its basic principle is expounded, and it is used in image recognition, and simulation results are given. The research shows that this method can effectively solve various problems in image recognition, and can effectively improve the recognition effect. Therefore, the algorithm proposed in this paper is helpful to improve the performance of artificial intelligence and contribute to the popularization of this technology. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Artificial Intelligence image Recognition Intelligent Algorithm

来源：评论

学校读者我要写书评

暂无评论

Deep learning-based image segmentation and text recognition model for oracle original topographies

Deep learning-based image segmentation and text recognition ...

引用

2024 International conference on Mechatronic Engineering and Artificial Intelligence, MEAI 2024

作者： Cai, Zhuoyan Wang, Lewei School of Mechanical Engineering Dalian Jiaotong University Dalian China

ISBN: (纸本)9781510689176

Oracle bone inscriptions, as the oldest mature form of the Chinese character system, carry the deep heritage of traditional Chinese culture, however, its recognition process is extremely complex and full of challenges. In this paper, we focus on the processing and recognition of the original topographic images of oracle bone inscriptions. First, an image preprocessing model is constructed, which can accurately recognize and effectively remove the noise in the image by virtue of the powerful function of Gaussian filter. At the same time, the model also adopts a non-local homogenization method for different texture features, which further improves the image quality. Then, a Mask R-CNN(Mask Region-based Convolutional Neural Network) model is introduced to train the image, and a cross-entropy loss function is employed to optimize the model performance, which successfully separates the clear oracle characters from the complex background and interference elements. Finally, an integrated method based on Mask R-CNN segmentation model and VGG(Visual Geometry Group) recognition model is proposed. The method significantly improves the recognition accuracy and efficiency of oracle bone characters through the process of segmentation followed by recognition, which provides a strong technical support for the in-depth development of oracle bone research. © 2025 SPIE.

关键词： image segmentation

来源：评论

学校读者我要写书评

暂无评论

Deep Learning Approach for Automated Railroad Ballast Condition Evaluation 5th

Deep Learning Approach for Automated Railroad Ballast Condit...

引用

5th International conference on Transportation Geotechnics

作者： Luo, Jiayi Ding, Kelin Qamhia, Issam I. A. Hart, John M. Tutumluer, Erol Univ Illinois Dept Civil & Environm Engn 205 N Mathews Urbana IL 61801 USA Univ Illinois Beckman Inst Adv Sci & Technol 405 N Mathews Urbana IL 61801 USA

ISBN: (纸本)9789819782154;9789819782130;9789819782123

Ballast has a significant impact on track performance, and the evaluation of ballast condition is crucial for safe railroad operations. This paper focuses on a Ballast Scanning Vehicle (BSV) recently developed for automating ballast inspection using a deep learning-based, computer vision approach. Traditional evaluation methods, e.g., visual inspection or ballast sampling followed by sieve analysis, are subjective and labor-intensive. Furthermore, ballast samples collected from a single location/depth may not be representative of accurately revealing variations of degradation and ballast condition along the track. In contrast, the BSV employs three image acquisition devices to continuously capture high-quality scans of ballast cut sections, enabling accurate and in-depth evaluation of continuous sections of the track. The deep-learning framework was trained to process acquired ballast scans, generating image-based metrics including percent degraded segments (PDS), fouling index (FI) estimates, and in-service ballast gradations. The accompanying user-friendly graphical interface integrates all data processing algorithms and provides comprehensive visualizations of results. Field data was collected using the BSV, from cut trenches opened using a ballast regulator, along the High Tonnage Loop (HTL) at the Transportation Technology Center (TTC) in the U.S. The FI and gradations from the BSV were compared to laboratory sieve analyses and Ground Penetrating Radar (GPR) data. Additional laboratory tests with various fouling conditions were conducted to validate the deep learning algorithms and clarify any differences between sieving results and algorithm estimates possibly attributed to sampling issues. This field deployment demonstrated that the BSV could accurately evaluate ballast conditions close to real time, thus making it a robust system for quantifying ballast degradation.

关键词： Railroad Ballast Fouling Inspection Track vehicle image segmentation Deep learning

来源：评论

学校读者我要写书评

暂无评论

Design and in-orbit application of Ku-band high resolution intelligent SAR satellite system

Design and in-orbit application of Ku-band high resolution i...

引用

2024 International conference on Remote Sensing and Digital Earth, RSDE 2024

作者： Chang, Wujun Jiang, Xiupeng Gao, Enyu Yan, Wei Song, Jiexuan Guo, Shuai Wu, Jiang Li, Yanming Jia, Zhihui Su, Fan Beijing Minospace Technology Co. Ltd. Beijing100094 China Xi’an Institute of Space Radio Technology Xi’an710100 China The Institute of Spacecraft Application System Engineering CAST Beijing100094 China

ISBN: (数字)9781510688223

ISBN: (纸本)9781510688216

TaiJing-4(03) satellite is the world's first high resolution flat-panel Ku-band synthetic aperture radar (SAR) commercial satellite, characterized by the high resolution, light weight design, low cost, multiple imaging modes and long-term operation. A series of key technologies and innovations are adopted, such as lightweight & high resolution Ku-band SAR payload, on-board intelligent imaging processing, non-uniform pulse sparse reconstruction of high resolution and wide width SAR imaging technology, target detection and recognition capability improvement, high precision and high stability attitude measurement and control, 2D guidance composite control, and multi-target imaging efficiency improvement. It achieves high resolution, high image quality and high image acquisition efficiency. The Ku-band SAR satellite has stronger perception of target details, and has multiple workmodes such as spotlight, sliding spotlight, strip, scanning, wide-width scanning, and multi-target imaging, which significantly improves satellite observation performance, coverage capability and application efficiency. It has important applications in high-resolution observation, emergency disaster relief, marine environment monitoring, resource investigation, etc. © 2025 SPIE.

关键词： Synthetic aperture radar

来源：评论

学校读者我要写书评

暂无评论

Adaptive Multi-modal Fusion Based Face Anti-spoofing with RGB-D images 18th

Adaptive Multi-modal Fusion Based Face Anti-spoofing with RG...

引用

18th Chinese conference on Biometric Recognition, CCBR 2024

作者： Teng, Zhan Fang, Wei Liu, Zhanli Chen, Lixi School of Intelligent Engineering and Automation Beijing University of Posts and Telecommunications Beijing China Megahunt Technologies Inc. Beijing China

ISBN: (纸本)9789819610679

The rapid advancement of face recognition technology has underscored the importance of face anti-spoofing techniques to ensure the security of these systems. While numerous deep learning-based face anti-spoofing methods have been proposed, including single classifier approaches using RGB or depth data, those based on multimodal data (RGB, depth, and infrared) have demonstrated superior performance. However, the fusion strategy for multimodal information at the score level remains underexplored. To address this gap, we present a comprehensive workflow for face anti-spoofing detection and introduce an adaptive fusion strategy for RGB and depth scores considering image quality. This approach effectively mitigates the limitations of individual RGB and depth models, enhancing their robustness against various attack types in diverse environments. We validate our method’s efficacy through tests on the CASIA-SURF, and 3DMAD datasets, comparing it with other methods. Furthermore, we demonstrate the system’s performance in real-world scenarios by testing in a realistic environment. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： image fusion

来源：评论

学校读者我要写书评

暂无评论

15th International Workshop on Computational Diffusion MRI, CDMRI 2024, held in conjunction with 27th International conference on Medical image Computing and Computer-Assisted Intervention, MICCAI 2024

15th International Workshop on Computational Diffusion MRI, ...

引用

15th International Workshop on Computational Diffusion MRI, CDMRI 2024, held in conjunction with 27th International conference on Medical image Computing and Computer-Assisted Intervention, MICCAI 2024

ISBN: (纸本)9783031869198

The proceedings contain 19 papers. The special focus in this conference is on Computational Diffusion MRI. The topics include: Ground-Truth Effects in Learning-Based Fiber Orientation Distribution Estimation in Neonatal Brains;synthesizing 3D Axon Morphology: Springs are All We Need;Randomly COMMITting: Iterative Convex Optimization for Microstructure-Informed Tractography;AID-DTI: Accelerating High-Fidelity Diffusion Tensor Imaging with Detail-Preserving Model-Based Deep Learning;multi-dimensional Parameter Space Exploration for Streamline-Specific Tractography;cross-Domain Fiber Cluster Shape Analysis for Language performance Cognitive Score Prediction;can Transfer Learning Improve Supervised Segmentation of White Matter Bundles in Glioma Patients?;image quality Transfer of Diffusion MRI Guided By High-Resolution Structural MRI;QID2: An image-Conditioned Diffusion Model for Q-Space Up-Sampling of DWI Data;Ts-FWE: Token-Aware Single-Shell Free Water Estimation for Brain Diffusion MRI;Assessing Early Motor system Degeneration in the Spinal Cord of ALS Patients Using Diffusion MRI: An Exploratory Study;RobNODDI: Robust NODDI Parameter Estimation with Adaptive Sampling Under Continuous Representation;introducing QuantConn: Overcoming Challenging Diffusion Acquisitions with Harmonization;Learning Low-Rank Tensor Approximation for GPU-Based Tractography;deep Multivariate Autoencoder for Capturing Complexity in Brain Structure and Behaviour Relationships;heritability and Genetic Correlations Along the Corticospinal Tract;corpus Callosum Parcellation Methods: What Can Tractography Tell Us About Them?.

关键词：

来源：评论

学校读者我要写书评

暂无评论

image quality Assessment for Deep Learning-Enabled Saffron Adulteration Detectors 5th

Image Quality Assessment for Deep Learning-Enabled Saffron A...

引用

5th International conference on Computing, Communications, and Cyber Security, IC4S 2023

作者： Manzoor, Suhail Selwal, Arvind Sabha, Ambreen Department of Computer Science and IT Central University of Jammu Jammu India

ISBN: (纸本)9789819773701

Saffron, also known as the "golden spice", is one of the costly spices cultivated mainly in Iran, Greece, Spain, China, and India (mostly in Kashmir valley). However, due to its limited production and high price, it is often subjected to the issue of adulteration which leads to economic loss and several health issues. The various types of adulterants that are mixed with original saffron samples are sunflower, safflower, maize outer covering, and hibiscus stigmas to increase their quantity. Nowadays, the menace of saffron adulteration is one of the major issues that need further attention as human vision fails to discriminate between pure and adulterated saffron samples. Hence, an intelligent computer vision-enabled saffron adulteration prediction system is used by researchers to overcome this issue. In this paper, we present a novel framework for saffron adulteration prediction using intelligent data-driven approaches. Moreover, various pre-processing techniques enhance the quality of saffron images. Besides, we demonstrate the comparative analysis of recent saffron adulteration prediction techniques and identify certain open research issues that need futuristic attention. In addition, performance protocols such as performance metrics and saffron images-based data are briefly illustrated. Our analysis reveals that this study provides researchers with new directions that need to be addressed in the future and can potentially prevent economic loss and ensure consumer safety. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Convolutional neural networks

来源：评论

学校读者我要写书评

暂无评论

没有更多数据了...

全选清除本页清除全部题录导出标记到“检索档案”

共500页 << < 1 2 3 4 5 6 7 8 9 10 > >>

检索报告对象比较合并检索0

隐藏清空

合并搜索

回到顶部

执行限定条件

内容：

评分：

请选择保存的检索档案：

请选择收藏分类：

订阅名称：

通借通还

温馨提示：

图书名称：

借书校区：

取书校区：

手机号码：

邮箱地址：

一卡通帐号：

电话和邮箱必须正确填写，我们会与您联系确认。

联系人：

所在院系：

联系邮箱：

联系电话：

内蒙古自治区呼和浩特市赛罕区大学西街235号邮编: 010021

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：